Chan, Tze-Haw and Lye, Chun Teck and Hooy, Chee-Wooi (2010): Forecasting Malaysian Exchange Rate: Do Artificial Neural Networks Work?
Download (260kB) | Preview
Being a small and open economy, the stability and predictability of Malaysian foreign exchange are crucially important. However, despite the general failure of conventional monetary models, foreign exchange misalignments and authority intervention have both caused the forecasting process an uneasy task. The present paper employs the monetary-portfolio balance exchange rate model and its modified version in the analysis. We then compare two Artificial Neural Networks (ANNs) estimation procedures (MLFN and GRNN) with random walk (RW) in the modeling-prediction process of RM/USD during the post-Bretton Wood era (1990M1-2008M8). The out-of-sample forecasting assessment reveals that the ANNs have outperformed the RW, which in particular, the MLFNs outperform GRNNs where as the latter outperform the RW models with consistency in both the exchange rate models by all evaluation criteria. In addition, the findings also show that the modified model has superior forecasting performance than the first model. In brief, economic fundamentals are vital in forecasting and explaining the RM/USD exchange rate. The findings are beneficial in policy making, investment modeling as well as corporate planning.
|Item Type:||MPRA Paper|
|Original Title:||Forecasting Malaysian Exchange Rate: Do Artificial Neural Networks Work?|
|English Title:||Forecasting Malaysian Exchange Rate: Do Artificial Neural Networks Work?|
|Keywords:||Artificial Neural Networks, Forecasting, modified monetary-portfolio balance model, RM/USD|
|Subjects:||C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods; Simulation Methods
C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C45 - Neural Networks and Related Topics
F - International Economics > F3 - International Finance > F31 - Foreign Exchange
|Depositing User:||Tze-Haw Chan|
|Date Deposited:||03. Nov 2010 08:30|
|Last Modified:||11. Feb 2013 21:19|
Adya, M., and Collopy, F. (1998) “How Effective are Neural Networks at Forecasting and Prediction? A Review and Evaluation” Journal of Forecasting 17, 481-495.
Baharumshah, A. Z. and Venus K.-S. Liew. (2005) “Forecasting Performance of Exponential Smooth Transition Autoregressive Exchange Rate Models” Open Economies Review 17, 261 – 277.
Bishop, C.M. (1995) “Neural Networks for Pattern Recognition” Oxford University Press, NY, 1995.
Cao, L., and Tay, F. (2001) “Financial forecasting using support vector machines” Neural Computing and Applications 10, 184-192.
Chen, A.S., and Leung M.T. (2001) “Performance evaluation of neural network architectures: the case of predicting foreign exchange correlations” Proceedings of the Decision Sciences Institute, 2001.
Cybenko, G. (1989) “Approximations by superpositions of a sigmoidal function” Mathematics of Control, Signals, and Systems 2, 303–314.
Gencay, R. (1999) “Linear, non-linear, and essential foreign exchange rate prediction with simple technical trading rules” Journal of International Economics 47, 91–107.
Granger, C.W.J. and Newbold, P. (1986) “Forecasting Economic Time Series” San Diego: Academic Press.
Hagan, M.T., and Menhaj, M. (1994) “Training feedforward networks with the Marquardt algorithm,” IEEE Transactions on Neural Networks, 5(6), 989-993.
Hammerstrom, D. (1993) “Neural networks at work” IEEE Spectrum, June, 26–32.
Hill, T., O’Connor, M., and Remus, W. (1996) “Neural network models for time series forecasts” Management Science 42, 1082-1092.
Hornik, K., Stinnchcombe, M., and White, H. (1989) “Multi-layer feed forward networks are universal approximators” Neural networks 2, 359–366.
Hu, M. Y., Zhang, G., Jiang, C. Y., and Patuwo, B. E. (1999) “A cross validation analysis of neural networks out-of-sample performance in exchange rate forecasting” Decision Sciences 30(1), 197–216.
Hush, D.R., and Horne, B.G. (1993) “Progress in supervised neural networks: What’s new since Lippmann?” IEEE Signal Processing Magazine, January, 8–38.
Kamruzzaman, J., and Sarker R.A. (2004) “ANN-Based Forecasting of Foreign Currency Exchange Rates” Neural Information Processing - Letters and Reviews 3(2), 49-58.
Kuan, C., and Liu, T. (1995) “Forecasting exchange rates using feed forward and recurrent networks” Journal of Applied Econometrics 10, 347–364.
Leung, M.T., Chen, A.S., and Daouk, H. (2000) “Forecasting exchange rates using general regression neural networks” Computers and Operations Research 27(11), 1093-1110.
Mizrach, Bruce. (1995) Forecast Comparison in L2, Working Paper, Department of Economics, Rutgers University.
Nasr, G.E., Dibeh, G., and Abdallah, M. (2006) “Modelling Exchange Rates during Currency Crisis using Neural Networks” Applied Simulation and Modelling, Proceeding 522.
Nikola, G., and Jing, Y. (2000) “The Application of Artificial Neural Networks to Exchange Rate Forecasting: The Role of Market Microstructure Variables” Bank of Canada Working Paper 2000, 23.
Panda, C, and Narasimhan, V. (2007) “Forecasting exchange rate better with artificial neural network” Journal of Policy Modeling 29, 227-236.
Powell, M.J.D. (1987) “Radial basis functions for multivariable interpolation: A review” in Algorithms for the the approximation of functions and data, Mason, J.C., and Cox, M.G., eds., Clarendon Press, Oxford, England, 143-167.
Rumelhart, D.E., Durbin, R., Golden, R., and Chauvin, Y. (1995) “Backpropagation: the basic theory” in Backpropagation: Theory, Architectures, and Applications, Chauvin, Y., and Rumelhart, D.E., eds., Lawrence Erlbaum Associates, New Jersey, 1–34.
Specht, D.F. (1991) “A general regression neural network” IEEE Transactions on Neural Networks 2(6), 568-576.
Wittkemper, H., and Steiner, M. (1996) “Using neural networks to forecast the systematic risk of stocks” European Journal of Operational Research 90, 577-589.
Wong, F.S. (1991) “Time series forecasting using backpropagation neural networks” Neurocomputing 2, 147–159.
Yao, J., and Tan, C.L. (2000) “A case study on using neural networks to perform technical forecasting of forex” Neurocomputing 34, 79-98.
Yaser, S., and Atiya, A. (1996) “Introduction to Financial Forecasting” Applied Intelligence 6, 205-213.
Yu, S.W. (1999), “Forecasting and Arbitrage of the Nikkei Stock Index Futures: An Application of Backpropagation Networks” Asia-Pacific Financial Markets 6, 341–354.
Zhang, G., and Hu, M.Y. (1998) “Neural network forecasting of the British pound/US dollar exchange rate” International Journal of Management Science 26(4), 495–506.
Zhang, G., Patuwo, B.E., and Hu, M.Y. (1998) “Forecasting with artificial neural networks: The state of the art” International Journal of Forecasting 14, 35–62.